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From Swift to Mojo and high-performance AI Engineering with Chris Lattner

By Gergely Orosz · The Pragmatic Engineer · · 6 min read

Deep Dives

Explore related topics with these Wikipedia articles, rewritten for enjoyable reading:

  • Autoconf 10 min read

    Linked in the article (5 min read)

  • LLVM 13 min read

    LLVM is the compiler infrastructure Chris Lattner created that underpins both Swift and Mojo. Understanding its architecture explains why these languages can achieve high performance and how compiler technology enables modern programming language development.

  • Swift (programming language) 12 min read

    The article extensively discusses Swift's creation, Apple's initial resistance, and how it democratized iOS development. A deep dive into Swift's design philosophy, type system, and evolution provides essential context for understanding Lattner's approach to Mojo.

Stream the latest episode

Listen and watch now on YouTube, Spotify, and Apple. See the episode transcript at the top of this page, and timestamps for the episode at the bottom.

Jump to interesting parts:

  • 20:28 — The story of how Swift was created

  • 47:28 — Chris’s AI learnings from working at Google and Tesla

  • 52:24 — The Mojo programming language’s origin story (Mojo is new, a high-performance, Python-compatible programming language)

  • 1:19:00 — AI coding tools the Modular team uses (Chris’s current startup)

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In this episode

Chris Lattner is one of the most influential engineers of the past two decades. He created the LLVM compiler infrastructure and the Swift programming language – and Swift opened iOS development to a broader group of engineers. With Mojo, he’s now aiming to do the same for AI, by lowering the barrier to programming AI applications.

I sat down with Chris in San Francisco, to talk language design, lessons on designing Swift and Mojo, and – of course! – compilers. It’s hard to find someone who is as enthusiastic and knowledgeable about both compilers, and programming, as Chris is!

We also discussed why experts often resist change even when current tools slow them down, what he learned about AI and hardware from his time across both large and small engineering teams, and why compiler engineering remains one of the best ways to understand how software really works.

A quote from the episode

“I believe in the power of programmers. I believe in the human potential of people that want to create things. And that’s fundamentally why I love software is that you can create anything that you can imagine.”

Interesting learnings from this episode

This episode was full of interesting details. Some of my favorite ones:

  1. “Compiles are cool. Don’t let anyone tell you otherwise.” My favorite

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